Internet cookies and (1) Identity and (2) Role

All residents of “the village”, whether prisoners or not, wear a “penny farthing page” with their number. Their activities are tracked much like a cookie on a web site. Residents, like users for popular web sites, are assigned a number. Residents become their number. “Number 2” is the titular head of the village (while Number 6 tries to discover who Number 1 is). The person assigned to Number 2 changes from episode to episode akin to multiple people using the same log-in and computer, and hence, the same cookie.
The identity is all about “role”. Something McLuhan predicted: the transition from jobs to roles for electronic man. Our roles change faster than in the 1960s – and without the benefit of badges to tell us what role we should be playing.Identity is always accompanied by violence, according to McLuhan. The number assigned to residents in The Prisoner defined the conflict in the narrative – from Number 2 (every Number 2) laughing evilly to Number 6’s wanting to be free. Identify violence has metastasized to social media flaming. This makes for good drama.

(5) Humans vs. (6) Machine Conflict and predictive analytics

The advent of mainframe computers generated the popular cultural stereotype: the all-seeing, all knowing machine. The 1957 comedyDesk Set best presented this notion of man vs. the machine. (In this case, Katherine Hepburn vs. Spencer Tracy’s machine). This conflict is presented as predictive analytics are used to determine residents’ behaviour. After all, they have resident badges and track movement. They’ve collected more elements of behaviour than the last Obama campaign.

Of course, they couldn’t process all those data points in 1967 – but we can now on the Amazon cloud. (The computer, in pre-Deep Bluedays, predicted the outcome of chess matches.)
Much like today, the village computer was not able to 100% predict resident behaviour. Number 6 understood that he was being analyzed, so he became unpredictable. The village computer seemed to have more trouble with the impact of social relationships in the same way that collaborative filtering can generate some very odd recommendations because the algorithm doesn’t understand the context.
The other machine problem is that the authorities were operating out of network – in the broadcast mode. They watched and they made announcements. They sent spies. But, they did not interact as peers with the residents. This is another problem experienced by governments and large business in the Internet age: you can’t always control the flow of information.
Marshall McLuhan suggested that we lived in a “state of information overload.” In 1967. There is far more information today challenging emerging big data techniques. McLuhan predicted a switch to pattern recognition in the post-literate generation. My sense is that the post-literate generation uses emoticons and abbreviations to better see patterns in the noise. http://www.freebalance.com/blog/?p=3488